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Predicting Defect Prone Modules in Web Applications

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Information and Software Technologies (ICIST 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 538))

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Abstract

Predicting defect proneness of software products has been an active research area in software engineering domain in recent years. Researchers have been using static code metrics, code churn metrics, developer networks, and module networks as inputs to their proposed models until now. However, domain specific characteristics of software has not been taken into account. In this research, we propose to include a new set of metrics to improve defect prediction performance for web applications by utilizing their characteristics. To validate our hypotheses we used datasets from 3 open source web applications to conduct our experiments. Defect prediction is then performed using different machine learning algorithms. The results of experiments revealed that overall performance of defect predictors are improved compared to only using existing static code metrics. Therefore we recommend practitioners to utilise domain-specific characteristics in defect prediction as they can be informative.

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Notes

  1. 1.

    Fault is used interchangeably in this study with the terms ‘defect’, ‘bug’ or ‘error’ to mean a fault in software code.

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Authors and Affiliations

  1. Computer Engineering Department, Yıldız Technical University, Istanbul, Turkey

    Mehmet Serdar Biçer & Banu Diri

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  1. Mehmet Serdar Biçer
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  2. Banu Diri
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Correspondence to Mehmet Serdar Biçer .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Giedre Dregvaite

  2. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damasevicius

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Biçer, M.S., Diri, B. (2015). Predicting Defect Prone Modules in Web Applications. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2015. Communications in Computer and Information Science, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-24770-0_49

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  • DOI: https://doi.org/10.1007/978-3-319-24770-0_49

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